Elsevier

Journal of Systems and Software

Volume 64, Issue 3, 15 December 2002, Pages 173-181
Journal of Systems and Software

An efficient broadcast data clustering method for multipoint queries in wireless information systems

https://doi.org/10.1016/S0164-1212(02)00039-0Get rights and content

Abstract

Mobile computing has become a reality with the convergence of two technologies: powerful portable computers and wireless networks. The restrictions of wireless networks, such as bandwidth and energy limitations make data broadcasting an attractive data communication method. This paper addresses the clustering of wireless broadcast data for multipoint queries. By effective clustering of broadcast data, the mobile client can access the data on the air in short latency. In the paper, we define two affinity measures: data affinity and segment affinity. The data affinity is the degree that two data objects are accessed by queries, and the segment affinity is the degree that two sets of data (i.e., segments) are accessed by queries. Our method clusters data objects based on data and segment affinity measures. We show that the performance of our method is scarcely influenced by the growth of the number of queries.

Introduction

With the recent advances of wireless communication and portable computing devices, a new type of information system, called a wireless information system, has evolved (Barbara, 1999; Imielinski et al., 1994). In wireless information systems, mobile clients can access the information in the remote server via wireless communication.

Wireless systems have some properties that are different from conventional wire-line systems. Some of them are energy limitation and bandwidth limitation. Owing to these properties, data broadcasting methods are widely used in wireless systems (Imielinski and Badrinath, 1994). In data broadcasting, mobile clients just receive broadcasted data without requests to servers. Because the energy usage in sending data is much bigger than that in receiving data, data broadcasting is energy-efficient with respect to mobile clients. Data broadcasting is also bandwidth-efficient, for many clients share one broadcasting channel.

In data broadcasting, we have to consider two performance aspects: access time and tuning time. The access time is the duration from the query start time to the time when all required data records are received at the client. And, the tuning time is the actual tune-in time (i.e., energy-usage time) for receiving data in the period of access time. In the past, there have been some approaches to reduce access time or tuning time. The scheduling (Acharya et al., 1995) and caching (Barbara and Imielinski, 1994; Wu et al., 1996) of wireless broadcast data reduce the access time, and the indexing (Imielinski and Viswanathan, 1994; Imielinski et al., 1997; Chung and Kim, 2000) of wireless broadcast data reduces tuning time.

In the paper, we consider clustering of wireless broadcast data for multipoint queries, that is, a query accesses one or more data records on the broadcast stream. On clustering of wireless data for multipoint queries, we proposed two methods (Chung and Kim, 2001a; Chung and Kim, 2001b) in the past. (As far as we are aware of, there is no other work on this area.) In our previous methods, we clustered wireless data records in units of query data set (i.e., the set of data that a query access). But their performance is degraded in case of a large number of queries. Note that, the attractiveness of data broadcasting is from the fact that it does not depend on the number of mobile clients (i.e., mobile queries). Therefore we, in this paper, propose a new wireless data clustering method for multipoint queries that is less dependent on the number of queries.

The paper is organized as follows. Section 2 introduces some background information on data broadcasting in wireless systems and describes our previous methods. In Section 3, we propose a new clustering method for wireless data broadcasting. The performance evaluation is described in Section 4. Finally, we conclude the paper in Section 5.

Section snippets

Clustering of wireless broadcast data

The clustering of wireless broadcast data is to determine the sequence of data to broadcast. We call the determined sequence of data a broadcast schedule, denoted by σ. In the paper, we assume the data records in a broadcast schedule are not replicated, that is, the broadcasting frequencies of all broadcasted data records are one. The server repeatedly broadcasts the stream of data records, commonly called bcast. Thus, if a client cannot access a data in the current bcast, the client has to try

The proposed method

One of useful properties of data broadcasting is that it is independent of the number of clients, for clients do not send requests to a server but just receive the broadcast stream. Thus, in wireless information systems, data broadcasting is widely used because it is known as an effective approach to manage a large number of mobile clients. However, if the broadcast data clustering method depends significantly on the number of mobile clients, it is a serious problem. (Here, we can regard the

Performance evaluation

In this section, we evaluate the performance of the proposed method through experiments in comparison with the SEM and GCM described in Section 2. To the best of our knowledge, there is no other work that deals with the clustering of wireless broadcast data for multipoint queries. The performance metric to be considered in experiments is the average access time of queries. We use the Zipf distribution (Gray et al., 1994; Knuth, 1981) for data sets of queries and consider the following

Conclusion

In the paper we addressed the clustering of wireless broadcast data for multipoint queries i.e., a single query accesses more than one data record. The previous clustering methods cluster the wireless broadcast data in units of a query’s data set, thus they show poor performance when there are a large number of query patterns and the queries’ frequency values are similar.

To solve this problem, we proposed a new clustering method that clusters the wireless broadcast data in consideration of the

Yon Dohn Chung received his B.S. degree in Computer Science from Korea University, Seoul, Korea, in 1994, and his M.S. and Ph.D. degrees in Computer Science from Korea Advanced Institute of Science and Technology (KAIST), Taejon, Korea, in 1996 and 2000, respectively. He is currently a research professor of Division of Computer Science at KAIST. His current primary research interests include mobile distributed systems, XML, bioinformatics and database systems.

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Cited by (0)

Yon Dohn Chung received his B.S. degree in Computer Science from Korea University, Seoul, Korea, in 1994, and his M.S. and Ph.D. degrees in Computer Science from Korea Advanced Institute of Science and Technology (KAIST), Taejon, Korea, in 1996 and 2000, respectively. He is currently a research professor of Division of Computer Science at KAIST. His current primary research interests include mobile distributed systems, XML, bioinformatics and database systems.

Su Ho Bang received his B.S. degree in Computer Science from Yonsei University, Seoul, Korea, and his M.S. degree in Computer Science from KAIST, Taejon, Korea, in 1999 and 2001, respectively. He is currently a researcher of eR&D Team, Korea Trade Network Inc., Seoul, Korea. His research interests include mobile computing, internet computing and eBusiness framework.

Myoung Ho Kirn received his B.S. and M.S. degrees in Computer Engineering from Seoul National University, Seoul, Korea, in 1982 and 1984, respectively, and his Ph.D. degree in Computer Science from Michigan State University, East Lansing, MI, in 1989. In 1989 he joined the faculty of the Division of Computer Science at KAIST, Taejon, Korea, where currently he is a professor. His research interests include database systems, OLAP, mobile computing, data mining, information retrieval and distributed processing. He is a member of the ACM and the IEEE Computer Society.

This work was supported in part by HV research center.

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